A single-molecule method for measuring fluorophore labeling yields for the study of membrane protein oligomerization in membranes

Membrane proteins are often observed as higher-order oligomers, and in some cases in multiple stoichiometric forms, raising the question of whether dynamic oligomerization can be linked to modulation of function. To better understand this potential regulatory mechanism, there is an ongoing effort to quantify equilibrium reactions of membrane protein oligomerization directly in membranes. Single-molecule photobleaching analysis is particularly useful for this as it provides a binary readout of fluorophores attached to protein subunits at dilute conditions. However, any quantification of stoichiometry also critically requires knowing the probability that a subunit is fluorescently labeled. Since labeling uncertainty is often unavoidable, we developed an approach to estimate labeling yields using the photobleaching probability distribution of an intrinsic dimeric control. By iterative fitting of an experimental dimeric photobleaching probability distribution to an expected dimer model, we estimate the fluorophore labeling yields and find agreement with direct measurements of labeling of the purified protein by UV-VIS absorbance before reconstitution. Using this labeling prediction, similar estimation methods are applied to determine the dissociation constant of reactive CLC-ec1 dimerization constructs without prior knowledge of the fluorophore labeling yield. Finally, we estimate the operational range of subunit labeling yields that allows for discrimination of monomer and dimer populations across the reactive range of mole fraction densities. Thus, our study maps out a practical method for quantifying fluorophore labeling directly from single-molecule photobleaching data, improving the ability to quantify reactive membrane protein stoichiometry in membranes.

The aim of this work is interesting (and this reviewer appreciates it), the paper is well done in almost all parts.
The developed method and experiments performed are interesting (and this reviewer appreciates it), the protocol is well described in all parts.
The manuscript in the present form demands a light revision before it can be published, so this reviewer suggests a minor revision of the paper.
The paper should be modified in some parts.
Major issues: 1) The abstract is too much long and results not clear, please reduce and improve.
Thank you for pointing this out. We have revised the abstract to elaborate on the results to hopefully make this clearer. In addition, the abstract is now shortened to 237 words, adhering to the journal guidelines.
2) Please adds the errors as bar in all graphs reported (i.e., Fig. 1

panel C is missed),
We revised the figures to improve visibility of error bars that were not visible in the previous version. Originally, we did not include error bars in Fig 1C because these plots reflect model data derived from a mathematical simulation. However, since this is a stochastic model where we randomly simulate protein partitioning into liposomes, we can report the variability associated with the results, even though the standard deviation in the data are small. We have now modified Fig 1C to include these error bars that reflect the simulation variability.

3) Please add (in materials and methods) a short paragraph that describe the statistical analysis used,
In the "Materials and methods" section, we have added a final section titled "Statistical analyses" that describes the type of errors calculated throughout, and the statistical tests that we used (page 12-13, lines 322-459). In addition, we modified Fig 4C to report mean ± sem from independent KD estimations per sample, rather than the standard deviation of the parameter estimation. The new representation allows for statistical testing between means that will be more useful for readers of this study.

4) Please divide the Discussion section in two sections (Discussion and Conclusion)
The discussion is now divided into "Discussion" and "Conclusion" sections.

5) Which is the operational range of your methods? Please explicit the constrains and limits in the conclusion
This is an excellent question and we thank the reviewer for asking this as we neglected to include this important result in the previous version of our manuscript. We have now revised the paper to include a new Fig 5, which shows a statistical analysis of the operational range for these studies based on the ability to discriminate monomer and dimer photobleaching probability distributions. With this, we conclude that for mole fraction densities of = 10 -9 to 10 -5 subunits/lipid, that Pfluor ³ 0.7 and Pbg £ 0.1 allow for significant discrimination between populations. However, if the reaction can be well described for £ 10 -6 subunits/lipid, then the labeling conditions can be lowered to Pfluor ³ 0.4, Pbg £ 0.1. Despite this, we comment that labeling should be optimized as close to Pfluor to 1 and Pbg to 0 to increase robustness in this photobleaching approach. This analysis is now described in a new section of the results on page 21-22, lines 741-826, Fig 5, and in the "Conclusions" section on page 26, lines 1030-1036.

Minor issue: 1) Please revise the text for some misspelling
We have now carefully read through the manuscript and revised the text for misspelling.